An AI agent is software powered by a large language model (LLM). The agent reads a goal, plans the steps and takes action through tools. Tools let the agent look up a customer, update a record, send a message or book a meeting.
A chatbot tells you how to do a task. An AI agent does the task and reports back when the job is done.
How an AI agent works
Every AI agent has four parts:
- A model. The LLM reads requests and decides what to do. Examples: Google Gemini, OpenAI GPT models and Anthropic Claude.
- Tools. Functions the agent is allowed to call, such as "find employee", "create invoice", "send WhatsApp message" or "check stock". Tools connect the agent to your systems.
- Context. The conversation so far, your business rules and the data returned by tools.
- Guardrails. Rules about what the agent must never do, when the agent asks a person, and which users trigger which actions.
When a request arrives, the agent runs a loop:
- Understand the request.
- Plan the next step. Most of the time, the first step is a lookup.
- Act by calling a tool.
- Check the result against the goal.
- Respond, or repeat the loop until the job is done.
find_documents tool and lists three employees. Next, the agent proposes a document request by WhatsApp and email. Nothing goes out until the manager presses Confirm.The assistant in our AI HR Portal follows this exact flow.
AI agents vs chatbots
Chatbots talk. Agents act. A traditional chatbot follows scripted flows or answers from a knowledge base. An AI agent reasons across several steps, uses your systems and finishes tasks end to end.
Read the full comparison, including workflow automation, in AI agents vs chatbots vs AI automation.
Types of AI agents businesses use
Customer support agents
Support agents answer questions on WhatsApp, your website and email at any hour. They check order or booking status and pass the conversation to a person when needed. See our customer support agent guide.
Sales and lead agents
Sales agents reply to new leads in seconds, ask qualifying questions, log each lead in your CRM and book the next step. See AI sales agents.
HR and operations assistants
Managers ask for what they need in plain language: find a record, add a new hire, request missing documents, set a reminder. A person confirms every change.
Research and content agents
Content agents watch sources, pick relevant stories, write first drafts and create images. Our NewsAgent runs this workflow for a news desk, with editors approving every post.
Document processing agents
Document agents read passports, ID cards, invoices and contracts, pull out the key fields and file them in the right system. See AI document processing.
Internal knowledge assistants
Knowledge assistants answer staff questions from your own policies, manuals and data. Staff stop searching folders and chat history.
What you gain
- 24/7 coverage. The agent replies at 2 a.m. and at 2 p.m.
- Speed. Lookups and routine actions take seconds instead of minutes.
- Consistency. The agent applies the same rules for every user.
- Scale. Volume grows without a matching rise in headcount.
- Focus. Your team spends less time on copy-paste work and more time on customers.
Risks and how to manage them
Every agent needs careful design. Here are the risks we plan for on each project:
| Risk | What happens | How to manage the risk |
|---|---|---|
| Made-up answers | The model guesses a fact or an ID | Force lookups through tools. Never act on memory |
| Wrong actions | The agent updates the wrong record | Require human confirmation for each change. One action at a time |
| Data exposure | Users see data outside their role | Apply your app's role-based permissions. Self-host when needed |
| Runaway cost | Thousands of extra model calls | Set daily limits, cache results and pick the right model per job |
| Frustrated customers | The agent fails to solve the problem | Offer a fast, clear handover to a person |
Our rule: automation proposes, people decide. Keep the rule in place until the agent proves reliable on a task.
How to start
- Pick one job. "Answer delivery-status questions on WhatsApp" works better than "an AI for customer service".
- List the systems. CRM, order database, calendar, document store.
- Write the guardrails. Decide which actions the agent is allowed to take and which stay off limits.
- Start read-only. Let the agent look up data and answer first.
- Add actions with confirmation. Next, let the agent create, update and send, with a person approving each action.
- Measure and expand. Track resolution rate, response time and hours saved. Then add the next job.
Need help with any step? Our AI agent development team builds custom agents for WhatsApp, web, email and internal tools.